Explanations of how to solve a task often involve a summary of the key decisions required to complete it, an ability referred to as selectivity [1]}, [2]}. Classic Deep Reinforcement Learning (RL) approaches lack this ability because all actions are reported when executing a policy. Recently, Complementary Temporal Difference Learning (CTDL) has been proposed which uses a Deep Neural Network (DNN) and a Self-Organizing Map (SOM) to solve the RL problem. Importantly, CTDL uses the errors produced by the DNN to update the contents of the SOM. In effect this results in the SOM storing episodic memories of states and actions that led to the largest errors during learning. We therefore use the contents of the SOM to generate task-level explanations as they provide an intuitive summary of most the important state-action pairs for solving the task at hand.
